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Low Latency Brain-Machine Interfaces

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Motor-imagery Brain-Computer Interfaces (MI-BCIs) aim to establish a communication channel between the human brain and an external device, solely based on human’s thought. The device aims to recognize the human's intentions from spatiotemporal neural activity typically recorded by a large set of electroencephalogram (EEG) electrodes. What makes it particularly challenging is to reliably detect the user's intention to perform MI with a short delay. There exist two types of EEG features, which can be used to detect motor intention: sensorimotor rhythms (SMR) and movement-related cortical potentials (MRCP). SMR-based system make use of event-related synchronisation/desynchronisation (ERD/ERS) behaviour and come with relatively long latency. Conversely, MRCP may be done with very short latency, possibly even with negative delay. This project analyses the delay of reliable MI onset detection using either SMR or MRCP features.

Status: In Progress

Student: She Xinyang

Supervision: Michael Hersche, Xiaying Wang, Simone Benatti, Victor Javier Kartsch


  • Machine Learning
  • Python


20% Theory
80% Implementation


Luca Benini

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